The increase of decentralized social media systems provides them with liberty and openness, yet tends to interfere with privacy because transaction data is made publicly accessible in blockchains. In this paper, a Privacy-Preserving Consensus Mechanism (PPCM) has been proposed as a privacypreserving blockchain in anonymous decentralized social media systems. To preserve the confidentiality and integrity of transactions, the PPCM incorporates the advanced cryptography solutions, Zero-Knowledge Proofs (ZKPs), Homomorphic Encryption, and ring signatures. It utilizes a Decentralized Identity (DID) model of self-sovereign identity management and cross-platform nteroperability, and overlays a reputation-based layer of governance to encourage ethical behaviour without disclosing the identity of users. Scalability is ensured with sidechains, which remove high-frequency interaction points of the main blockchain to minimize latency. The model is a compromise between privacy and accountability and solves such issues as Sybil attacks, metadata leakage, and unethical use of anonymity. The PPCM offers a privacy-focused, scalable, and ethically regulated design of next-generation decentralized social media networks.
The rapid advancement of creating Zero-Knowledge (ZK) programs has led to the development of numerous tools designed to support developers. Popular options include being able to write in general-purpose programming languages like Rust from Risc Zero. Other languages exist like Circom, Lib-snark, and Cairo. However, developers entering the ZK space are faced with many different ZK backends to choose from, leading to a steep learning curve and a fragmented developer experience across different platforms. As a result, many developers tend to select a single ZK backend and remain tied to it. This thesis introduces zkSDK, a modular framework that streamlines ZK application development by abstracting the backend complexities. At the core of zkSDK is Presto, a custom Python-like programming language that enables the profiling and analysis of a program to assess its computational workload intensity. Combined with user-defined criteria, zkSDK employs a dynamic selection algorithm to automatically choose the optimal ZK-proving backend. Through an in-depth analysis and evaluation of real-world workloads, we demonstrate that zkSDK effectively selects the best-suited backend from a set of supported ZK backends, delivering a seamless and user-friendly development experience.
<sec> <title>BACKGROUND</title> The convergence of AI, Blockchain (BC) technology, and healthcare represents one of the most transformative but technically challenging frontiers in computational medicine. As healthcare systems worldwide transition toward data-driven paradigms for precision medicine, clinical decision support, and population health management, the imperative for secure, privacy-preserving, and collaborative learning frameworks has reached critical importance. This tutorial presents the first comprehensive framework integrating Federated Learning (FL) and BC} for secure, privacy-preserving healthcare analytics. While FL offers collaborative training across distributed institutions without raw data sharing (aligning with HIPAA/GDPR), it faces vulnerabilities like model poisoning and gradient leakage. We introduce Blockchain-based Federated Learning (BCFL), leveraging BC's immutable ledger and decentralized consensus for enhanced trust, verifiability, and auditability. Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully, semi, loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance; (3) comprehensive security analysis of healthcare-specific vulnerabilities and mitigation via advanced cryptography like zero-knowledge proofs, homomorphic encryption and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI/Achine-Learning (ML) medical devices. We demonstrate BCFL's effectiveness across critical healthcare applications (e.g., disease prediction, medical imaging, patient monitoring, drug discovery) and identify emerging research frontiers including quantum-resilient cryptography, scalable interoperability, healthcare-specific incentives, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, collaborative AI in healthcare, accelerating privacy-preserving analytics, and ultimately improving patient outcomes. </sec> <sec> <title>OBJECTIVE</title> The objective of the paper is to present the first comprehensive tutorial on integrating Federated Learning (FL) and Blockchain (BC) technologies specifically for secure, privacy-preserving healthcare analytics. The motivation stems from the growing need for collaborative healthcare data analysis that adheres to stringent privacy regulations like HIPAA and GDPR, especially as traditional centralized models pose significant data security risks. The authors aim to address the vulnerabilities of FL, such as model poisoning and gradient leakage, by leveraging BC’s features like decentralization, immutability, and auditability. The tutorial is designed to guide researchers, practitioners, and policymakers in understanding and implementing secure AI systems in the medical domain. </sec> <sec> <title>METHODS</title> To achieve this goal, the authors develop a multi-faceted framework by first creating a comprehensive taxonomy of medical data types and their specific requirements for FL deployment. They then propose three novel integration architectures—fully coupled, semi-coupled, and loosely coupled—each analyzed for its security, scalability, and compliance with healthcare regulations. The tutorial includes an in-depth security analysis addressing threats unique to healthcare, and explores privacy-enhancing technologies such as zero-knowledge proofs, homomorphic encryption, and differential privacy. It also introduces a regulatory compliance framework aligned with HIPAA, GDPR, and FDA guidelines for AI/ML-based medical devices. Throughout, the methodology integrates technical depth with practical implementation advice. </sec> <sec> <title>RESULTS</title> The results of this study are delivered through a set of clearly articulated contributions. The proposed architectures and frameworks are demonstrated to significantly enhance trust, verifiability, and auditability in healthcare FL systems, making them more robust against known threats. The paper effectively showcases how BCFL (Blockchain-based Federated Learning) can be applied to real-world healthcare use cases such as disease prediction, patient monitoring, medical imaging, and drug discovery. Additionally, it outlines emerging research directions, including quantum-resilient cryptography, scalable interoperability, incentive mechanisms for healthcare data sharing, and automated compliance monitoring. These outcomes position the tutorial as a foundational reference for advancing secure and compliant collaborative AI in healthcare. </sec> <sec> <title>CONCLUSIONS</title> This tutorial presented the first comprehensive framework integrating FL and BC for secure, privacy-preserving healthcare analytics. We demonstrated how FL enables decentralized model training across healthcare institutions while maintaining data locality, and how BC enhances trust, integrity, and auditability through immutable ledgers and decentralized consensus mechanisms. Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully coupled, semi-coupled, and loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance trade-offs; (3) comprehensive security analysis identifying healthcare-specific vulnerabilities and mitigation strategies using advanced cryptographic techniques including zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a practical regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI}/ML-based medical devices. We validated BCFL effectiveness across critical healthcare applications including disease prediction, medical imaging analysis, patient monitoring, and drug discovery. Looking ahead, crucial research frontiers involve quantum-resilient cryptography, scalable interoperable infrastructure, healthcare-specific consensus mechanisms, and automated compliance frameworks. This tutorial serves as a foundational reference for developing trustworthy, interoperable, and patient-centric AI systems that transform healthcare delivery while ensuring privacy protection and regulatory compliance. The successful realization of secure collaborative healthcare analytics through BCFL will drive improved patient outcomes and accelerate medical discoveries in an increasingly connected healthcare ecosystem. </sec> <sec> <title>CLINICALTRIAL</title> N/A </sec>
Divyanshu Pabia, Manasvi Rao Kanukolan, A. Anbarasi
Conventional FASTag and similar tolling networks rely on centralized clearinghouses that invite insider fraud, introduce single points of failure, and expose motorists’ movement data. This paper presents a fully decentralized architecture that migrates the entire transaction path-RFID tag detection, tariff computation, signature-verified debit, and final settlement-onto Ethereum via the ERC-4337 account-abstraction standard. Per-vehicle smart-contract wallets are deterministically generated from each vehicle identifier and execute an atomic UserOperation, producing an immutable audit trail while eliminating custodial databases. Anonymous authentication is achieved through a Groth16 zero-knowledge circuit derived from Anon-Aadhaar, which discloses only a one-time nullifier, thereby preventing replay attacks and preserving user privacy. A protocol-compliant Paymaster contract sponsors gas, enabling “tap-and-go” usability without requiring drivers to hold cryptocurrency. Existing UHF RFID hardware and EPC Gen-2 slotted-ALOHA anti-collision logic is preserved; scan events are simply notarized on-chain, rendering tampering computationally infeasible. By fusing account abstraction, zk-SNARK-based anonymous verification, and gas-sponsored execution, the proposed framework delivers a tamper-proof, privacy-preserving, and outage-resilient tolling solutionmodernizing infrastructure without imposing additional financial or technical burdens on motorists or operators.
As the frequency and scope of major diseases continue to rise, the need for an efficient early warning and prevention system in public health has become increasingly urgent. This paper addresses the challenges of preventing and predicting high-frequency and sudden-onset diseases, and proposes a blockchain-based solution to construct a trusted data space. The solution integrates blockchain technology with trusted data space construction, effectively addressing the challenges of data sharing and utilization across regions, departments, and business domains for disease warning and prevention. The experiment showed that the solution on-chain TPS(Transactions Per Second) for spatial data is 1318.7, and the single-node QPS(Queries Per Second) is 1999.2. It meets the requirements for handling high-frequency and sudden public health events, and offers certain advantages in data security, sharing efficiency, and privacy protection. Future research will continue to explore the deep integration and extended applications of cross - chain technology, zero - knowledge proof, and other privacy -preserving computing technologies.
Internet of Thing is a promising technology for creating smart home systems. Devices are being added gradually in the smart home's environments, causes the significant challenges into security, scalability, compatibility, Interoperability, etc. Traditional centralized authentication methods are not able to keep the dynamic and diverse nature of these smart environment. To address these challenges, we proposed an adaptive mutual authentication scheme with Zero Knowledge Proof and machine learning integrated within blockchain based key management and storage system. The proposed approach used Elliptic Curve Cryptography technique for key generation, a consortium blockchain for storing keys and device metadata, and a hybrid encryption scheme adaptively choosing between AES-GCM and ChaCha20-Poly1305 based on IoT device capabilities. Machine learning model is integrated to predicts the optimal cryptographic parameters, and also to ensure both security and resource efficiency. The proposed mutual authentication scheme provides a secure and scalable model for smart home systems.
Vittoria Bonanzinga, Mariantonia Cotronei, Gioia Failla, Sofia Giuffré · 5 authors
The increasing use of localization devices for location-based services has led to an explosion in user location data. This raises significant privacy concerns that often conflict with the need for identification and accountability in critical scenarios like criminal investigations or public health emergencies. Research is facing the challenge of balancing privacy with data utility, guaranteeing trust in verification. This paper proposes a novel blockchain-based solution to reconcile the conflicting requirements of user privacy and accountability in localization. Our scheme leverages the transparency and immutability of blockchain to record verifiable location proofs. To ensure user privacy against routine disclosure, the solution integrates elliptic curve cryptography and Zero-Knowledge Proofs, allowing a verifier to confirm a user's presence without revealing sensitive information. Our solution also prevents the verifier from disclosing proof of a user's past presence to third parties, further enhancing privacy. Moreover, the proposed system provides a mechanism for accountability, allowing a designated authority to override privacy safeguards and access location data when legally mandated for public interest reasons, thereby reconciling privacy and identification needs.
G. Ramesh, Utkarsh Anand, P. B. Edwin Prabhakar, Akhilesh Pahade · 5 authors
Depending on past results, data manipulation, centralized control, and fraud could be problems with either digital or hand-voting systems. In a democratic state, everyone lacks honest and safe voting systems. Here, we present a fresh consensus approach for Proof of Eligibility and Identity (PoEI). The intended users of this system are distributed voting applications emphasizing security and forward-looking needs. This approach guarantees eligibility and enables anonymous voting by combining a custom permissioned blockchain with a zero-knowledge proof (ZKP)- based identity verification system. Furthermore, the approach guarantees the preservation of eligibility. While smart contracts manage voter registration, ballot submission, and automated tallying, cryptographic audit trails are responsible for increasing operational transparency. A virtual municipal election, which included 10,000 candidates, was conducted to confirm the approach. The election was an apparent success, given that there was no space for repeated voting and a transaction latency of less than a second. These findings suggest that the proposed model can replace current blockchain consensus systems, providing verifiability, tamper-proofness, and scalability. Ensuring its security, this work prepares for future electoral modernization grounded on distributed technologies.
Dilli Ganesh, T J Nandhini, Amer Ibrahim, Ahmed A. Elngar · 5 authors
With the current prevalence of digitization of health care records comes the issues of data privacy, security, and interoperability typical in traditional health information systems. This paper proposes a Blockchain-Powered Secure Health Data Exchange that utilizes smart contracts, cryptographic algorithms (AES-256, ECDSA), and a decentralized ledger to improve patient privacy and interoperability. This paper presents a novel blockchain-based monitoring mechanism tailored for EHRs: RUDDER—real-time, universal, decentralized, distributed, and enciphered data regulation for EHRs. Using role-based access control (RBAC) and zero-knowledge proofs (ZKP), the architecture prevents unauthorized access in our patient-centric model. This approach enabled the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which offers high transaction throughput and latency. In addition, we create an interoperability layer that is FHIR compliant and allows for continued data exchange between the hospitals, research institutions, and the insurer. Experimental results show that significant gains have been achieved with a 500% increase in scalability, 99.6% lower operational costs, and 90% lower energy consumption compared to their conventional counterparts. The new framework that was proposed is a scalable, secure, and cost-effective solution for next-generation healthcare data management. The future work will cover AI-based anomaly detection and quantum-resistant cryptography that can improve security and efficiency.
Tholfiqar Z. Ismail, Mehdi Ebady Manaa, Durbek Sayfullaev, Muhidinov Ayubbek Nuritdinovich · 6 authors
Electronic voting systems are increasingly being explored to enhance accessibility, efficiency, and speed in modern electoral processes. However, ensuring vote integrity, privacy, and auditability remains a major challenge, especially in remote and online voting environments. Traditional electronic voting methods often face issues such as data tampering, lack of end-to-end verifiability, and potential privacy breaches, which undermine public trust and electoral transparency. To address these issues, this paper proposes the ZK-VOTE (Zero-Knowledge Verified Online Tamper-resistant Election) framework, which integrates zk-SNARKs with a permissioned blockchain protocol. In this system, voters generate zero-knowledge proofs to verify their eligibility and the validity of their votes without disclosing sensitive information. Each vote is immutably recorded on a permissioned blockchain, ensuring transparency while maintaining voter anonymity. The use of consensus algorithms prevents unauthorized alterations to voting records, and smart contracts automatically enforce vote submission rules. The ZK-VOTE framework is particularly suited for national-scale elections, enabling secure remote voting for diaspora populations while ensuring system-wide auditability. Election authorities and third-party auditors can independently verify election results without accessing private voter data. Experimental evaluation and theoretical analysis demonstrate that the proposed method achieves high levels of privacy, resistance to tampering, and verifiability. Results confirm that ZK-VOTE enhances voter trust and electoral transparency while remaining computationally efficient. The framework represents a significant advancement toward secure, scalable, and trustworthy electronic voting systems.
Ashu Nayak, S. Sivasubramanian, Anil Sharma, Hasan M. Madi · 7 authors
The rising need for safe and privacy-preserving digital identity verification has exposed the limits of previous methods, which typically involve personal information. These technologies risk users' privacy owing to data leaks and user-centric management issues. Due to these problems, this article presents ZK-VerifyChain, a blockchain-based Zero-Knowledge-based Verification system. The proposed ZK-VerifyChain uses permissioned blockchain smart contracts and non-interactive zero-knowledge proofs to verify identities securely. With this design, users can identify themselves without giving personal information. The suggested technique was tested in a virtual environment for computational overhead, scalability, and verification time. Testing has demonstrated that the ZK-Verify Chain is effective, fast, and secure against manipulation and identity theft. The blockchain layer provides immutability, transparency, and decentralized trust management. The suggested ZK-VerifyChain moves us closer to user-controlled, secure digital identity systems by providing a scalable, privacy-centric digital identity verification solution. The experimental results show an average verification time of 12.4 ms compared to other methods.
In this modern world, every field has transitioned from traditional written to digital records. Fields such as healthcare involve sensitive digital records; ensuring secure access to them while protecting patients’ privacy is crucial. This paper provides a novel approach for a secure access control system that employs blockchain with zero-knowledge proofs (ZKP) and facial recognition to enhance the security of sensitive records. Face verification is used as biometric-based identity verification, and hence, captured images need to be processed securely. By encrypting such data on the client side and storing user biometric data, the system’s design prioritizes user privacy. It ensures that biometric data is protected while maintaining standard access control features. The system utilizes ZKP to authenticate users without revealing their private keys, ensuring robust authentication. Incorporating blockchain technology decentralizes the proof verification process and acts as a potential way to achieve tamper-proof storage, thus reducing the possibility of unauthorized access. Experimental results show that the proposed system achieves face verification and secure access validation 2x and 1.5x faster than the previous approaches. By undergoing a detailed design, extensive evaluation, and validation of the proposed system, this paper demonstrates the system’s security and provides seamless access to authorized healthcare professionals.
The fundamentals of blockchain build trust in its security, consistency, and decentralised structure. For secure transactions, maintaining integrity, and trustless consensus among network users, cryptography is deployed in the blockchain. Blockchain enjoys immutability and secure environments by using cryptographic tools like hash functions, digital signatures, and public key cryptography. These methods give the guarantee of secure and verifiable data transfer. In addition, cryptography provides the convenience of designing distributed and decentralised ledgers, which enables safe peer-to-peer transactions without the requirement for a central authority. Homomorphic encryption and zero-knowledge proof types of ideas that can improve the privacy and scalability of blockchain applications are also included in this chapter. Cryptography not only helps the blockchain in performing essential duties but also propels technical advancement by enabling new use cases and wider industry acceptance.
Hiba Akli, Igor Stéphan, Karim Zkik, Sofiane Hamrioui
E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.
Cryptocurrencies, particularly Bitcoin, continue to be the most prevalent use case within the blockchain ecosystem. One of the inherent limitations of blockchain is that it can create a false sense of privacy. All transaction history and the amount of cryptocurrency held are publicly available, and this information can be easily associated with specific individuals. Many works have proposed fully-private solutions, which are ideal but not realistic in many scenarios. This paper proposes a technical solution that enables private Bitcoin payments by default, but with the option to conditionally disclose payment data. To do so, this solution relies on unlinkability by a decentralized mixer, which can be reversed by a conditional discloser using a trapdoor unlinkability function. The conditional discloser, which also provides accountability of requests, obeys the payer's policies regarding who can access payment data. To ensure compliance, we propose a mixer that does not learn anything about the payment link, but is guaranteed by Zero-Knowledge Proofs that the payment can be relinked by a specific conditional discloser. Furthermore, we provide a proof-of-concept implementation of the proofs, using Circom and SnarkJS. We also present a benchmark that demonstrates the feasibility of this solution. It incurs only one additional parameter per on-chain transaction, while the remainder of the verification data is managed off-chain.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
A powerful feature in mechanism design is the ability to irrevocably commit to the rules of a mechanism. Commitment is achieved by public declaration, which enables players to verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that the mechanism designer might prefer not to disclose, such as her target function or private costs. Avoiding this may be possible via a trusted mediator; however, the availability of a trustworthy mediator, especially if mechanism secrecy must be maintained for years, might be unrealistic. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome—all without the need for any mediators. Our framework is based on zero-knowledge proofs—a cornerstone of modern cryptographic theory. Applications include both private-type settings such as auctions and private-action settings such as contracts, as well as non-mediated bargaining with hidden yet binding offers.
The Internet of Things (IoT) has become an integral part of daily life, making the protection of user privacy increasingly important. In gateway-based IoT systems, user data is transmitted through gateways to platforms, pushing the data to various applications, widely used in smart cities, industrial IoT, smart farms, healthcare IoT, and other fields. Threshold Public Key Encryption (TPKE) provides a method to distribute private keys for decryption, enabling joint decryption by multiple parties, thus ensuring data security during gateway transmission, platform storage, and application access. However, existing TPKE schemes face several limitations, including vulnerability to quantum attacks, failure to meet Simulation-Security (SS) requirements, lack of verifiability, and inefficiency, which results in gateway-based IoT systems still being not secure and efficient enough. To address these challenges, we propose a Verifiable Simulation-Secure Threshold PKE scheme based on standard Module-LWE (VSSTPM). Our scheme resists quantum attacks, achieves SS, and incorporates Non-Interactive Zero-Knowledge (NIZK) proofs. Implementation and performance evaluations demonstrate that VSSTPM offers 112-bit quantum security and outperforms existing TPKE schemes in terms of efficiency. Compared to the ECC-based TPKE scheme, our scheme reduces the time cost for decryption participants by 72.66%, and the decryption verification of their scheme is 11 times slower than ours. Compared with the latest lattice-based TPKE scheme, our scheme reduces the time overhead by 90% and 48.9% in system user encryption and decryption verification, respectively, and their scheme is 13 times slower than ours in terms of decryption participants.
Graph theory has emerged as a foundational mathematical tool in the realms of cryptography and network security. Its ability to model complex relationships, systems, and interactions through vertices and edges enables innovative solutions for encryption, authentication, key distribution, intrusion detection, and secure routing. This research article provides a comprehensive review of recent advancements and applications of graph-theoretical techniques in cryptographic protocols and secure network systems.The study begins by outlining the theoretical underpinnings of graph theory relevant to secure communications, including graph isomorphism, expander graphs, Hamiltonian paths, and graph coloring. It then explores how graph-based methods are utilized in modern cryptographic systems such as zero-knowledge proofs, public-key cryptography, and lightweight encryption schemes. The article also discusses graph-theoretic approaches in blockchain consensus models, attack graph analysis, intrusion detection systems (IDS), and secure routing in wireless sensor networks (WSNs).Recent advancements such as post-quantum cryptography based on hard graph problems, dynamic attack graphs in adaptive security systems, and trust graphs in distributed environments are highlighted. Data from peer-reviewed publications from 2010 to 2025 are synthesized, and key trends are visualized through tables, graphs, and diagrams. The paper also identifies existing challenges, including scalability, computational complexity, and graph-theoretical attack vectors.The discussion critically interprets these findings, connects them to existing literature, and proposes directions for future research, including graph-based AI models for threat prediction and hypergraph frameworks for modeling higher-order trust relationships.Overall, this study offers an integrated perspective on how graph theory continues to transform the cryptographic and security landscape, contributing to the development of resilient, efficient, and scalable secure systems.
Jiaxiang Sun, Rong Zhao, Lehao Lin, Yuanfang Chi · 6 authors
The popularity of artificial intelligence (AI)-generated content (AIGC) has experienced significant growth recently. Despite AIGC’s potential to transform content creation in various industries, its dependence on extensive computational resources poses a challenge for widespread adoption. To address this challenge, AIGC as a service has been proposed. However, concerns related to dataset compliance have emerged as a source of apprehension among stakeholders. The complexities associated with manual supervision, apprehensions regarding data leakage, and the potential for malicious behavior by third-party supervisors collectively present formidable challenges in the regulation of datasets within the domain of AIGC service. To tackle these challenges, this article presents a blockchain-based system for regulating AIGC datasets. Our proposed system employs AI, zero-knowledge proofs, and smart contracts as integral components for overseeing dataset compliance. To assess the feasibility and effectiveness of the proposed system, a comprehensive analysis and a series of simulations have been conducted. These evaluations offer valuable insights into the system’s security, privacy, and performance.
Ensuring secure and efficient authentication in Vehicular Ad Hoc Networks (VANETs) is vital for real-time communication and network resilience. However, traditional authentication mechanisms, such as Elliptic Curve Cryptography (ECC) and Public Key Infrastructure (PKI), face significant challenges, including high computational overhead, complex certificate revocation, and vulnerability to quantum attacks. To overcome these limitations, we propose a lattice-based authentication protocol that integrates post-quantum cryptography (PQC), zero-knowledge proofs (ZKPs), and fog computing for secure Vehicle-to-Roadside (V2R) communication. Our protocol offers quantum resistance, decentralized authentication, and dynamic pseudonym updates, enhancing both security and privacy in VANETs. Performance evaluations demonstrate that our approach achieves lower message delay (0.8), reduced packet loss ratio (0.6), minimal communication overhead (0.7), and the fastest authentication delay (0.5) compared to ECC and Physically Unclonable Function (PUF)-based methods. Additionally, formal security analysis confirms that our scheme effectively mitigates impersonation, replay, tracking, and quantum attacks, ensuring a scalable and future-proof authentication mechanism for next-generation VANETs.
Zhi Lu, Mengyuan Zou, Samir M. Umran, Yuhao Long · 7 authors
Federated learning, as an emerging distributed machine learning approach, enables collaborative model training while protecting data privacy. However, federated learning is vulnerable to Byzantine attacks and inference attacks. Existing solutions typically require semi-honest servers to perform secure aggregation or lack effective input validation mechanisms. To address these issues, we propose RIDE, a secure aggregation protocol for decentralized federated learning with input validation. RIDE utilizes pedersen commitments and efficient zero-knowledge proofs to verify whether model updates comply with predefined constraints, ensuring client input privacy and integrity. Additionally, RIDE employs a publicly verifiable secret sharing scheme, ensuring that only validated model updates are aggregated, even in the presence of malicious clients or client dropouts. Experimental results on four real datasets demonstrate the effectiveness of our solution. For example, RIDE has a maximum bandwidth overhead of 7.11MB, which is only 1.31× that of the most popular secure aggregation protocol (CCS 2020), and the computational cost of RIDE’s execution on the CIFAR-10 L dataset is 109.88s, which is 7.28× faster than the current state-of-the-art protocol RoFL (S&P 2023).